The quality of health care is constantly evolving and improving as new less invasive surgical techniques, more effective medications, and better methods of treatment are constantly being discovered and invented. Improvements in health care have also occurred through better use and management of patient information. One such use has allowed medical personnel to reliably predict future probable conditions of a patient through trend analysis of the patient's information. Trends within various patient vital signs (e.g., blood pressure, heart rate, body temperature, etc.) have been shown to reliably indicate future medical conditions or complications.
Attempts have been made to create a standard or objective way to measure trends in a patient's condition by quantifying the results of such trends into one or more “severity scores”. Severity scores are usually developed by combined efforts from multiple healthcare organizations. Such efforts have the primary aim of quantifying patient illness such that mortality of an organization can be adjusted by considering the expected survival rate based on these severity scores as well as providing a reliable prognosis of probable changes in the condition of the patient. The severity scores thus assist in providing a quicker response to treat any such changes.
To be an objective measure requires that severity scores should be defined using patient information that may include laboratory test results, vital signs, etc. To achieve consistent scoring requires that definitions of severity scores should be clearly specified so that the processes used in the mapping of the vital signs to the severity score are enumerated.
Many studies have been done on validating existing severity scoring metrics. Severity scores such as Acute Physiology and Chronic Health Examination (APACHE) and Simplified Acute Physiology Score (SAPS) have been well known for purposes including mortality prediction and patient stratification. Other scores, such as the Modified Early Warning Score (MEWS), have been proposed for early detection of patient deterioration and have been validated in several pilot studies. However, the impact of these severity scores into daily clinical practice remains elusive because these severity scores have not been widely accepted and integrated into typical workflows of patient care for possible reasons including lack of an automated scoring system, ambiguities in terms of specification of data collection protocol for scoring, and lack of studies of applying severity scores to individual patients. More specifically, the barriers to the adoption of such severity scores include insufficient data gathering, time alignment issues resulting from inconsistent data gathering, and improper data processing (e.g., aggregation and unit conversion) as some examples.
Typically, the data reporting for such severity scoring is conducted on a manual basis by some medical personnel assigned the task to gather and aggregate such data. As a result, the reporting is at times inconsistent or subjective. Additionally, trend analysis may not include sufficient diversity of patients to accurately predict the probable outcome for all cases. For example, it is not clear whether the various existing scoring metrics can perform multiple scoring over larger sets of data points and whether or not the various existing scoring metrics can track temporal score changes.
Additional deficiencies in current severity scoring result from the delay associated with the data gathering and analysis. For instance, the existing scoring metrics only take recorded data after it has been manually transcribed from some vital sign monitor into a database. The time it takes for the data gathering to be completed and further still for the trend analysis to be completed can cause sufficient delay which reduces or defeats the effectiveness and potential early warning provided by the severity score.
The penetration of information technology (IT) into the various aspects of health care has assisted to alleviate some of the data gathering and data management overhead previously associated with providing health care. Establishment and wide adoption of industry-wide standards such as Health Level Seven (HL7) and Digital Imaging and Communications in Medicine (DICOM) together with the much improved computational capability, data storage capability, and fast communication platforms, have provided an ideal environment for the further development of more dedicated IT solutions tailored for more specific clinical challenges.
However, there is a need to better leverage information stored and managed by these IT resources to provide improved health care services to patients. Specifically, there is a need for a severity scoring system that is: (1) highly automated, (2) supports the computation of multiple scores simultaneously, and (3) supports both retrospective and online (i.e., real-time) modes of operation.
There is also a need to cut costs and better prioritize patient care. Hospitals generally have different units according to the level of monitoring and care provided to patients in the unit. Intensive care units (ICUs) provide the most monitoring to a patient, as there is often a one-to-one patient-to-nurse ratio. General wards provide the least amount of monitoring in a hospital. Intermediary “step-down” units provide less monitoring than an ICU, but more than a general ward. Substantial cost-savings are achieved when a patient is discharged from a unit with higher monitoring and moved to a unit with lower monitoring (or discharged from a hospital altogether). These cost savings can amount to thousands of dollars or more per day per bed vacated in an ICU. This cost may include fees of an intensivist—a specialized doctor who oversees an ICU. Because intensivists are relatively rare (approximately 1,200 intensivists compared to 5,000 hospitals in the United States), intensivists often oversee ICUs of several hospitals at once. Discharging a patient from an ICU would thusly result in both a substantial cost savings and an increase in the availability of intensivists.
The current practice of prioritizing patient care is time-consuming and inefficient, and may cause unnecessary delay in discharging a patient. Generally, medical interns perform daily “pre-rounds.” This consists of manually retrieving raw data for individual patients from various hospital systems. Interns create summaries, called rounding lists, for an attending physician to read through and make a determination regarding prioritizing the care of the patients on the list. Performing pre-rounds and generating rounding lists can often take upwards of one hour. Furthermore, the attending physician is relied upon to expend time and expertise in prioritizing the patients. Patients who are good candidates for discharge from a unit are often not discharged in a timely fashion because teams responsible for discharging patients are not privy to a real-time snapshot of the patient's condition. Furthermore, doctors' time is used in an inefficient manner in generating and evaluating rounding lists.
Therefore, there is a need in the art for methods and systems that enable healthcare providers to better monitor patients.